Decoding low density parity codes

Abstract

The present invention includes a technique for updating messages that originate at the constraint nodes of bi-partite graphs in Low Density Parity Check codes. The technique computes only two outgoing magnitudes at each constraint node and exhibits no measurable performance loss as compared to exact belief propagation which computes a unique magnitude for each departing edge from a given constraint node. The technique eliminates the need for memory based table look-up in the constraint node processing and has been implemented, in one embodiment, using only shift, add, and comparison operations.

Description

[0001]This application claims the benefit of U.S. Provisional Application No. 60 / 510,183, “Decoding Low Density Parity Codes” filed on Oct. 10, 2003, which is incorporated herein by reference in its entirety.[0002]This invention was made with Government support of Grant No. N00014-01-C-0016, awarded by the Office of Naval Research. The Government has certain rights in this invention.BACKGROUND OF THE INVENTION[0003]1. Field of the Invention[0004]The present invention is directed to technology relevant to decoding Low Density Parity codes.[0005]2. Description of the Related Art[0006]Communication technology has become more important and has received greater attention as more people communicate via computer networks, telephone networks and various wireless networks. The goal is to communicate data in a reliable and efficient manner. However, data being communicated can be corrupted. For example, wireless data (e.g. digital or analog, voice or non-voice) is subject to noise.[0007]There...

AI Technical Summary

Benefits of technology

The present invention is about decoding LDPC codes using a reduced complexity algorithm that suffers little or no performance loss. The system uses combinational logic rather than table look-up to approximate the non-linear portion of the constraint update. The technique eliminates the need for memory based table look-up in the constraint node processing and has been implemented, in one embodiment, using only shift, add, and comparison operations. The invention can be implemented using hardware or software, or a combination of both. The software can be stored on one or more processor readable storage devices or dedicated hardware. The technical effects of the invention include improved efficiency and accuracy in decoding LDPC codes.

Problems solved by technology

However, data being communicated can be corrupted.

For example, wireless data (e.g. digital or analog, voice or non-voice) is subject to noise.

First, there is ARQ, which includes re-transmitting data if there is a problem.

Second, there is Forward Error Correction (FEC), which includes using codes with the data to detect and, possibly, correct, corrupt data.

The problem being solved with FEC is how much redundancy to add in an intelligent manner to make sure that the data arrives correctly.

Long codes do a better job insuring correctness, but are more complex and harder to work with.

However, these codes were too short for the sphere packing bound to approach Shannon capacity, and the computational resources for longer random codes were decades away from being broadly accessible.

However, this form still posses significant processing challenges as it employs a non-linear function that must be represented with a large dynamic range for optimal performance.

Even Full-BP algorithms suffer performance degradation as compared to the optimum Maximum Likelihood (ML) decoder for a given code.

This is due to the fact that bipartite graphs representing finite-length codes without singly connected nodes are inevitably non-tree-like.

Cycles in bipartite graphs compromise the optimality of belief propagation decoders.

Establishing the true ML performance of LDPC codes with length beyond a few hundred bits is generally viewed as an intractable problem.

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